Effects of Social e-Commerce on Consumer Behavior

Ford Lumban Gaol, Mulia Denavi, Jonathan Danny, Bagas Ditya Anggaragita, Andry Hartanto, Tokuro Matsuo

Abstract


Objectives: The purpose of this research is to conduct an examination of intention factors for using social commerce in Indonesia. Methods/Analysis: This research is a quantitative study that applies the customer analysis model to four big social commerce sites in Indonesia. This study uses the multivariate regression method and IBM SPSS 25 software to prove the relationship between research variables. Findings: Variables will include performance expectations, effort expectations, societal effects, supportive circumstances, and cost savings. Data from 210 online respondents in Indonesia were collected. Novelty and Improvements:Positive outcomes are provided by the model as a result of changes in the use of social commerce.

 

Doi: 10.28991/HIJ-2022-03-04-01

Full Text: PDF


Keywords


Social Commerce; Multivariat Regression; Performance Expectancy; Effort Expectancy; Facilitating Condition; Social Influence; Price Saving.

References


Celuch, K., Goodwin, S., & Taylor, S. A. (2007). Understanding small scale industrial user internet purchase and information management intentions: A test of two attitude models. Industrial Marketing Management, 36(1), 109–120. doi:10.1016/j.indmarman.2005.08.004.

Anastasiadou, E., Chrissos Anestis, M., Karantza, I., & Vlachakis, S. (2020). The coronavirus’ effects on consumer behavior and supermarket activities: insights from Greece and Sweden. International Journal of Sociology and Social Policy, 40(9–10), 893–907. doi:10.1108/IJSSP-07-2020-0275.

Wang, E., An, N., Gao, Z., Kiprop, E., & Geng, X. (2020). Consumer food stockpiling behavior and willingness to pay for food reserves in COVID-19. Food Security, 12(4), 739–747. doi:10.1007/s12571-020-01092-1.

Liang, T. P., Ho, Y. T., Li, Y. W., & Turban, E. (2011). What drives social commerce: The role of social support and relationship quality. International Journal of Electronic Commerce, 16(2), 69–90. doi:10.2753/JEC1086-4415160204.

Huang, Z., & Benyoucef, M. (2013). From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications, 12(4), 246–259. doi:10.1016/j.elerap.2012.12.003.

Jiao, J. (2020). Analysis of the Current Situation and Development Trend of Mainstream Social E-Commerce in China. Proceedings of the Fifth International Conference on Economic and Business Management (FEBM 2020), 159, 607-611. doi:10.2991/aebmr.k.201211.105.

Sheth, J. (2020). Impact of Covid-19 on consumer behavior: Will the old habits return or die? Journal of Business Research, 117, 280–283. doi:10.1016/j.jbusres.2020.05.059.

Arora, N., Charm, T., Grimmelt, A., Ortega, M., Robinson, K., Sexauer, C., & Yamakawa, N. (2020). A global view of how consumer behavior is changing amid COVID-19. Mcknsey and Company, New York City, United States.

Caldas, M. P. (2003). Management information systems: managing the digital firm. Revista de Administração Contemporânea, 7(1), 223–223. doi:10.1590/s1415-65552003000100014.

Liang, T. P., & Turban, E. (2011). Introduction to the special issue social commerce: A research framework for social commerce. International Journal of Electronic Commerce, 16(2), 5–13. doi:10.2753/JEC1086-4415160201.

Esmaeili, L., Mutallebi, M., Mardani, S., & Golpayegani, S. A. H. (2015). Studying the affecting factors on trust in social commerce. arXiv preprint arXiv:1508.04048. doi:10.48550/arXiv.1508.04048.

Marsden, P., & Chaney, P. (2012). The social commerce handbook: 20 secrets for turning social media into social sales. McGraw Hill Professional, New York City, United States.

Gatautis, R., & Medziausiene, A. (2014). Factors Affecting Social Commerce Acceptance in Lithuania. Procedia - Social and Behavioral Sciences, 110(2013), 1235–1242. doi:10.1016/j.sbspro.2013.12.970.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. doi:10.2307/30036540.

Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information and Management, 48(1), 1–8. doi:10.1016/j.im.2010.09.001.

Jílková, P., & Králová, P. (2021). Digital Consumer Behaviour and eCommerce Trends during the COVID-19 Crisis. International Advances in Economic Research, 27(1), 83–85. doi:10.1007/s11294-021-09817-4.

Newsted, P. R., Huff, S. L., & Munro, M. C. (1998). Survey instruments in information systems. MIS Quarterly: Management Information Systems, 22(4), 553–554. doi:10.2307/249555.

Gupta, V., Cahyanto, I., Sajnani, M., & Shah, C. (2021). Changing dynamics and travel evading: a case of Indian tourists amidst the COVID 19 pandemic. Journal of Tourism Futures. doi:10.1108/JTF-04-2020-0061.

Sugiyono. (2016). Quantitative, Qualitative and R&D Research Methods. PT Alfabet, Bandung, Indonesia. (In Indonesian).

Jílková, P., & Králová, P. (2021). Digital Consumer Behaviour and eCommerce Trends during the COVID-19 Crisis. International Advances in Economic Research, 27(1), 83–85. doi:10.1007/s11294-021-09817-4.

Mohd Dali, N. R. S., Abdul Hamid, H., Wan Nawang, W. R., & Wan Mohamed Nazarie, W. N. F. (2020). Post pandemic consumer behavior: Conceptual framework, 17, 13–24. doi:10.33102/jmifr.v17i3.280.

Cruz-Cárdenas, J., Zabelina, E., Guadalupe-Lanas, J., Palacio-Fierro, A., & Ramos-Galarza, C. (2021). COVID-19, consumer behavior, technology, and society: A literature review and bibliometric analysis. Technological Forecasting and Social Change, 173, 121179. doi:10.1016/j.techfore.2021.121179.

Chauhan, V., & Shah, M. H. (2020). An empirical analysis into sentiments, media consumption habits, and consumer behaviour during the Coronavirus (COVID-19) Outbreak. Purakala, 31(20), 353-378. doi:10.13140/RG.2.2.32269.15846.

Hajli, N., & Sims, J. (2015). Social commerce: The transfer of power from sellers to buyers. Technological Forecasting and Social Change, 94, 350–358. doi:10.1016/j.techfore.2015.01.012.

Liu, M., & Wronski, L. (2018). Examining Completion Rates in Web Surveys via Over 25,000 Real-World Surveys. Social Science Computer Review, 36(1), 116–124. doi:10.1177/0894439317695581.

Alexander, D., & Karger, E. (2020). Do stay-at-home orders cause people to stay at home? Effects of stay-at-home orders on consumer behavior. The Review of Economics and Statistics, 1-25. doi:10.1162/rest_a_01108.


Full Text: PDF

DOI: 10.28991/HIJ-2022-03-04-01

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Ford Lumban Gaol, Mulia Denavi, Jonathan Danny, Bagas Ditya Anggaragita, Andry Hartanto, Tokuro Matsuo